Whole-brain functional connectivity-based prediction in ultra-high-risk for psychosis individuals
Poster No:
1436
Submission Type:
Abstract Submission
Authors:
Karen Ambrosen1, Tina Kristensen1, Louise Glenthøj2, Merete Nordentoft3, Birte Glenthøj1, Anita Barber4, Bjørn Ebdrup1
Institutions:
1Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup, Glostrup, Denmark, 2VIRTU Research Group, Mental Health Center Copenhagen, Copenhagen, Denmark, 3Copenhagen Research Centre for Mental Health (CORE), Mental Health Center Copenhagen, Copenhagen, Denmark, 4Department of Psychiatry, Zucker Hillside Hospital and Zucker School of Medicine at Hofstra/Northwel, New York, NY
First Author:
Karen Ambrosen
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Co-Author(s):
Tina Kristensen, PhD
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Merete Nordentoft
Copenhagen Research Centre for Mental Health (CORE), Mental Health Center Copenhagen
Copenhagen, Denmark
Copenhagen Research Centre for Mental Health (CORE), Mental Health Center Copenhagen
Copenhagen, Denmark
Birte Glenthøj, Professor
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Anita Barber
Department of Psychiatry, Zucker Hillside Hospital and Zucker School of Medicine at Hofstra/Northwel
New York, NY
Department of Psychiatry, Zucker Hillside Hospital and Zucker School of Medicine at Hofstra/Northwel
New York, NY
Bjørn Ebdrup, Professor
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Center for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup
Glostrup, Denmark
Introduction:
The ultra-high-risk state (UHR) for psychosis is a prodromal phase that may progress into frank psychosis. Identifying neurobiological alterations in this early stage could facilitate early intervention efforts to improve the long-term outcome. Resting-state functional magnetic resonance imaging (rs-fMRI) offers insights into the neural mechanisms underlying psychosis, particularly when associated with clinically relevant features. In this study, we examine the functional connectivity in UHR individuals compared to healthy controls, to identify brain networks affected in this vulnerable state of putative development of frank psychosis.
Methods:
We included 102 UHR individuals and 105 matched controls, aged 18-40 years, who underwent clinical assessments and rs-fMRI. UHR was defined by the intensity and frequency of attenuated psychotic symptoms, brief limited psychotic episodes, or state-and-trait vulnerability assessed with the Comprehensive Assessment of At-Risk Mental States (CAARMS) (Yung et al., 2005). Functional level was assessed using the Social and Occupational Functioning Assessment Scale (SOFAS) (Hilsenroth et al., 2000). Whole-brain functional connectivity was estimated from the rs-fMRI data using the 200-dimensional parcellation from Yan et al. (2023) and 54 subcortical regions defined by Tian et al. (2020). The network labels were obtained from Thomas Yeo et al. (2011). We tested the predictive power of functional connectivity using a prediction-based extension of the network-based statistics (NBS-predict) (Serin et al., 2021), a comprehensive machine learning framework. We aimed to predict diagnosis, level of functioning, estimated IQ, and symptom level.
Results:
Application of NBS-predict reveals brain networks with both hyper- and hypoconnectivity that significantly predicted diagnosis with an accuracy of 0.58 (95% CI: 0.57-0.59, p=0.043) and 0.59 (95% CI: 0.57-0.60, p=0.002), respectively. Hyperconnectivity was mainly observed in interhemispheric cortico-cortical connections and cortico-thalamic connections. The cortical regions involved were part of the somatomotor, the temporoparietal, the dorsal attention, and the default mode network (Figure 1A). Hypoconnectivity was mainly observed in thalamic connections to the posterior cingulate cortex and precuneus within the control network and to frontal medial regions within the salience ventral attention network (Figure 1B). Functional connectivity predicted the groupwise interaction effect on the level of functioning with a significant correlation between the predicted and measured level of functioning (ρ=0.34, 95% CI: 0.32-0.36, p<0.001). The interaction was mainly driven by UHR individuals showing positive associations between level of functioning and interhemispheric connectivity between the frontal medial regions involved in the salience ventral attention network, and between frontal medial regions and regions in left hemisphere within the somatomotor and dorsal attention network. Functional connectivity did not predict IQ or symptom level.
Conclusions:
UHR individuals displayed brain networks with both hyper- and hypoconnectivity, which significantly discriminated UHR individuals from healthy controls and related inversely to the level of functioning in the two groups. Hyperconnectivity between the thalamus and the somatomotor network has previously been reported in schizophrenia. The marked role of thalamus as a central hub for integration of information across networks is confirmed in our study (Hwang et al., 2021). Our results suggest that alterations in functional connectivity are present already in the prodromal phase, potentially reflecting underlying neurobiological changes that precede the onset of psychosis.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Keywords:
FUNCTIONAL MRI
Machine Learning
Schizophrenia
Thalamus
Other - Connectivity
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Hwang, K., Shine, J. M., Bruss, J., Tranel, D., & Boes, A. (2021). Neuropsychological evidence of multi-domain network hubs in the human thalamus. ELife, 10, 1–24. https://doi.org/10.7554/eLife.69480
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fisch, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011
Tian, Y., Margulies, D. S., Breakspear, M., & Zalesky, A. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nature Neuroscience, 23(11), 1421–1432. https://doi.org/10.1038/s41593-020-00711-6
Yan, X., Kong, R., Xue, A., Yang, Q., Orban, C., An, L., Holmes, A. J., Qian, X., Chen, J., Zuo, X. N., Zhou, J. H., Fortier, M. V., Tan, A. P., Gluckman, P., Chong, Y. S., Meaney, M. J., Bzdok, D., Eickhoff, S. B., & Yeo, B. T. T. (2023). Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity. NeuroImage, 273(October 2022), 120010. https://doi.org/10.1016/j.neuroimage.2023.120010
Yung, A. R., Yuen, H. P., McGorry, P. D., Phillips, L. J., Kelly, D., Dell’Olio, M., Francey, S. M., Cosgrave, E. M., Killackey, E., Stanford, C., Godfrey, K., & Buckby, J. (2005). Mapping the onset of psychosis: The Comprehensive Assessment of At-Risk Mental States. Australian and New Zealand Journal of Psychiatry, 39(11–12), 964–971. https://doi.org/10.1111/j.1440-1614.2005.01714.x